BUS708 Statistics and Data Analysis | KOI
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King's Own Institute (KOI), Australia
BUS708 Statistics and Data Analysis (BUS708)
Added on 2020-03-04
BUS708 Statistics and Data Analysis | KOI
King's Own Institute (KOI), Australia
BUS708 Statistics and Data Analysis (BUS708)
Added on 2020-03-04
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Introductiona)There is a hypothetical situation whereby a company which is a service provider to variousindividuals who have migrated to Australia either on temporary basis or on permanent basis. Oneof the key services that need to be provided is related to accommodation. In order to provideservices to the various clients regarding recommendation, the objective of this is to carry out ananalysis on the weekly rent for the residential properties based on the dataset of 500 suchproperties located in four suburbs namely Auburn, Randwick, Parramatta and Sydney.Additionally analysis of the weekly rent being paid by the international students residing inAustralia also needs to be estimated. b)The data collected for dataset 1 is primary and has been obtained from the international studentswhom I personally asked about the weekly rent. The data collected can be potentially biased as theunderlying geography, gender, economic status was not taken into consideration. Infact, thesample was based on convenience sampling as I obtained the rent values for those 20 studentswhom I came across and these students were not selected from a larger population which shouldideally be the case. Since the data has been collected from the students by talking to thempersonally, hence the data is primary. The variable involved is weekly rent which is a quantitativedata type with a ratio scale of measurement. The data is quantitative since it is captured innumerical terms while the ratio scale refers to the existence of a well-defined zero.c)The dataset 2 contains a sample of 500 observations which have been selected from the “RentalBond Board Property Data on weekly” rent collected in four suburbs by the ‘Department ofFinance, Services and Innovation’. This is a secondary dataset since this has been obtained from adataset which another agency has collected. The bond amount, number of bedrooms and weeklyrent are quantitative (numeric) data with ratio scale of measurement. However, the dwelling typeand suburb are qualitative (non-numeric) data with nominal scale of measurement. The first 5cases from the dataset are highlighted below.International Students’ Weekly Renta)The numerical summary of the international students’ weekly rent is presented in the form of summary statistics based on given sample of 20 students.
STATISTICS AND DATA ANALYSISThe graphical summary of the given data can be presented using a bar chart as indicated below.0123456Weekly Rent ($)Frequencyb)It is apparent from the summary statistics that the central tendency measures namely mean, modeand median are not the same which implies that the given distribution is not normal. This is furthersubstantiated by the presence of skew which needs to be zero for normal distribution. Also, thekurtosis value is not equal to 3 which is another requirement for normal distribution. Besides, thegraphical illustration also highlights the presence of a rightward tail or positive skew which iscaused due to the presence of outliers on the positive side. One of the outliers is $ 450 as theweekly rent. Further, due to the presence of the positive skew, it would be appropriate to concludethat median value would be a better measure of central tendency in comparison with the mean.Also, in relation to the dispersion, it would be fair to conclude that standard deviation is low whenviewed in perspective of the mean. Rental Bond Board Property Data – Dwelling Typea)The given data has been analysed with regards to the type of dwelling. This has been done with thehelp of pivot table. The relevant output obtained on the basis of the given data is indicated below.Further, the graphical illustration of the dwelling by type is indicated below.
STATISTICS AND DATA ANALYSISIt is apparent from the above that a high majority of dwelling type exists in the form of flats whilea small minority of the total dwellings exist in the form of houses (which comprise less than 6%).It is noteworthy that the above represents the summary of the sample data of 500 dwellings.b)The objective is to determine if the claim that house dwelling type has a proportion of less than10% is supported by the given data or not. The relevant hypotheses are indicated below.Null Hypothesis: p ≥ 0.10 i.e. the proportion of house dwelling type has a proportion which is not lessthan 10%Alternative Hypothesis: p < 0.10 i.e. the proportion of house dwelling type has a proportion which isless than 10%Sample probability = (29/500) = 0.058Standard error = √[0.1(1-0.1)/500] = 0.01342Calculated Z statistic = (0.058-0.1)/ 0.01342 = -3.13The corresponding p value for the above computed value of z statistic comes out as 0.0008.Taking 5% as the significance level, it is apparent that p value (0.0008) is significantly lower than theassumed significance level, hence there is presence of enough evidence to reject the null hypothesis.Thus, the alternate hypothesis is accepted which implies that the claim regarding the house dwellingtype comprising less than 10% of the total is correct.c)The graphical and numerical summary of dwelling as per suburb is captured below.AUBURNPARRAMATTARANDWICKSYDNEY0204060801001201401601802004013811917414951Bar Chart - Dwelling type and Suburb FlatHouseBased on the above, it is apparent that the flat seems to be predominant dwelling type in the varioussuburbs. It is only in Auburn where the representation of the house dwelling type is about 25%.
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